pandas read_json: "If using all scalar values, you must pass an index"

Try

ser = pd.read_json('people_wiki_map_index_to_word.json', typ='series')

That file only contains key value pairs where values are scalars. You can convert it to a dataframe with ser.to_frame('count').

You can also do something like this:

import jsonwith open('people_wiki_map_index_to_word.json', 'r') as f:    data = json.load(f)

Now data is a dictionary. You can pass it to a dataframe constructor like this:

df = pd.DataFrame({'count': data})

You can do as @ayhan mention which will give you a column base format

Or you can enclose the object in [ ] (source) as shown below to give you a row format that will be convenient if you are loading multiple values and planing on using matrix for your machine learning models.

df = pd.DataFrame([data])

I think what is happening is that the data in

map_index_to_word = pd.read_json('people_wiki_map_index_to_word.json')

is being read as a string instead of a json

{"biennials": 522004, "lb915": 116290, "shatzky": 127647, "woode": 174106, "damfunk": 133206, "nualart": 153444, "hatefillot": 164111, "missionborn": 261765, "yeardescribed": 161075, "theoryhe": 521685}

is actually

'{"biennials": 522004, "lb915": 116290, "shatzky": 127647, "woode": 174106, "damfunk": 133206, "nualart": 153444, "hatefillot": 164111, "missionborn": 261765, "yeardescribed": 161075, "theoryhe": 521685}'

Since a string is a scalar, it wants you to load it as a json, you have to convert it to a dict which is exactly what the other response is doing

The best way is to do a json loads on the string to convert it to a dict and load it into pandas

myfile=f.read()jsonData=json.loads(myfile)df=pd.DataFrame(data)